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A method for concrete crack detection using U-Net based image inpainting technique

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(10), pp.35-42
  • DOI : 10.9708/jksci.2020.25.10.035
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 24, 2020
  • Accepted : September 17, 2020
  • Published : October 30, 2020

Su-Min Kim 1 sohn jung mo 1 Do-Soo Kim 1

1이포즌

Accredited

ABSTRACT

In this study, we propose a crack detection method using limited data with a U-Net based image inpainting technique that is a modified unsupervised anomaly detection method. Concrete cracking occurs due to a variety of causes and is a factor that can cause serious damage to the structure in the long term. In general, crack investigation uses an inspector’s visual inspection on the concrete surfaces, which is less objective in judgment and has a high possibility of human error. Therefore, a method with objective and accurate image analysis processing is required. In recent years, the methods using deep learning have been studied to detect cracks quickly and accurately. However, when the amount of crack data on the building or infrastructure to be inspected is small, existing crack detection models using it often show a limited performance. Therefore, in this study, an unsupervised anomaly detection method was used to augment the data on the object to be inspected, and as a result of learning using the data, we confirmed the performance of 98.78% of accuracy and 82.67% of harmonic average (F1_Score).

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